Sci‐Thur PM – Colourful Interactions: Highlights 03: Radiation induced glycogen accumulation in non‐small cell lung cancer xenografts detected using Raman spectroscopy
Bibliographic record
Abstract
Purpose: This study presents the novel application of Raman spectroscopy (RS) to identify biochemical signatures of radiation response in human non‐small cell lung cancer (NSCLC) xenografts, irradiated in vivo. Methods: Human NSCLC cells (H460) were subcutaneously injected into the flanks of 12 mice. Tumours were treated with single fraction radiation doses (0, 5 or 15 Gy) and harvested at 3 days post irradiation. A Renishaw inVia Raman microscope coupled to a 785 nm laser was used to collect Raman spectral maps for each tumour. Immunohistochemistry (IHC) staining for CAIX was used to visualize hypoxia, and co‐registration between IHC fluorescence and Raman images was carried out. Results: Principal component analysis revealed radiation induced spectral signatures linked to changes in protein, nucleic acid, lipid and carbohydrates. In particular, a marked increase in glycogen for irradiated tumours was observed. Spatial mapping revealed intra‐tumoural heterogeneity in the distribution of glycogen within the tumour, suggesting tumour response to radiation is not globally uniform. Furthermore, co‐registration of Raman glycogen maps with CAIX IHC staining showed a correlation between glycogen rich and hypoxic regions of the tissue. Conclusions: We identify glycogen as a unique radiation induced response in NSCLC tumour xenografts, which may reflect inherent metabolic changes associated with radiation response in tissue. This study provides unique insight into the biochemical response of tumours, irradiated in vivo, and demonstrates the potential of RS for detecting radiobiological responses in tumours.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".